Technical Documentation: EricLBuehler/mistral.rs

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| <a href="https://ericlbuehler.github.io/mistral.rs/"><b>Documentation</b></a> | <a href="https://ericlbuehler.github.io/mistral.rs/quickstart/"><b>Quickstart</b></a> | <a href="https://ericlbuehler.github.io/mistral.rs/reference/supported-models/"><b>Supported models</b></a> | <a href="https://crates.io/crates/mistralrs"><b>Rust SDK</b></a> | <a href="https://ericlbuehler.github.io/mistral.rs/guides/python/getting-started/"><b>Python SDK</b></a> | <a href="https://discord.gg/SZrecqK8qw"><b>Discord</b></a> |
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Latest Copy Chapter

- Muse Glimmer 30B: native text, image, and video inference with ATEM tool calling, reasoning controls, LoRA, ISQ/UQFF, and companion-projector GGUF loading. Model notes
- GGUF loading: load a local file with -f, or select a published artifact with --quant. Tokenizer, configuration, and multimodal projector files are discovered when the available metadata identifies them unambiguously. Guide
- OpenAI-compatible Skills: upload /v1/skills bundles and reference them from Responses requests for reusable procedures, helper scripts, and local data. Guide
- OpenAI-compatible file inputs: upload /v1/files, attach Responses input_file or Chat file parts, and mount request files into shell/code sessions. Guide
- DiffusionGemma: block-diffusion text generation. Fully integrated: paged attention, prefix caching, ISQ, multimodal, and tool calling. Guide
- Anthropic Messages API: mistralrs serve now exposes Anthropic-compatible /v1/messages and /v1/messages/count_tokens endpoints alongside the OpenAI-compatible /v1 API. Guide
- v0.8.2 CUDA performance: paged-attention and MoE optimizations deliver strong results on GB10, B200, and H100 SXM. Benchmarks
- Agentic runtime: web search, local Python code execution, shell execution, OpenAI-compatible Skills, session management, and custom tool hooks. Guide
- Gemma 4: full multimodal: text, image, video, and audio input. Supported models | Video setup

Benchmarks Copy Chapter

<details>
<summary><b>v0.8.2 CUDA benchmarks</b></summary>

Mean tokens per second across prompt lengths and decode depths from 128 to 16384 tokens. Decode uses 256 generated tokens. See the full v0.8.2 report for commands, model revisions, host metadata, and appendix tables.

Q8 prefill TPS: mistral.rs UQFF q8 vs llama.cpp GGUF Q8_0

| Model | Hardware | mistral.rs | llama.cpp |
|---|---|---:|---:|
| Gemma 4 E4B | GB10 | 7395.7 | 3973.7 |
| Gemma 4 E4B | B200 | 27705.6 | 11992.4 |
| Gemma 4 E4B | H100 SXM | 26220.6 | 11702.1 |
| Gemma 4 26B-A4B | GB10 | 2947.0 | 2178.5 |
| Gemma 4 26B-A4B | B200 | 12725.3 | 8503.4 |
| Gemma 4 26B-A4B | H100 SXM | 12362.3 | 8055.1 |

Q8 decode TPS: mistral.rs UQFF q8 vs llama.cpp GGUF Q8_0

| Model | Hardware | mistral.rs | llama.cpp |
|---|---|---:|---:|
| Gemma 4 E4B | GB10 | 44.1 | 40.5 |
| Gemma 4 E4B | B200 | 241.4 | 194.4 |
| Gemma 4 E4B | H100 SXM | 223.1 | 183.0 |
| Gemma 4 26B-A4B | GB10 | 46.8 | 46.4 |
| Gemma 4 26B-A4B | B200 | 210.9 | 192.2 |
| Gemma 4 26B-A4B | H100 SXM | 199.8 | 183.9 |

BF16 prefill TPS: mistral.rs BF16 vs vLLM BF16

| Model | Hardware | mistral.rs | vLLM |
|---|---|---:|---:|
| Gemma 4 E4B | GB10 | 5838.9 | 5812.9 |
| Gemma 4 E4B | B200 | 43547.8 | 39431.2 |
| Gemma 4 E4B | H100 SXM | 35852.2 | 39293.7 |
| Gemma 4 26B-A4B | GB10 | 592.2 | 3878.6 |
| Gemma 4 26B-A4B | B200 | 3467.3 | 28532.8 |
| Gemma 4 26B-A4B | H100 SXM | 2766.0 | 26295.9 |

BF16 decode TPS: mistral.rs BF16 vs vLLM BF16

| Model | Hardware | mistral.rs | vLLM |
|---|---|---:|---:|
| Gemma 4 E4B | GB10 | 25.1 | 18.8 |
| Gemma 4 E4B | B200 | 202.6 | 196.2 |
| Gemma 4 E4B | H100 SXM | 174.4 | 153.0 |
| Gemma 4 26B-A4B | GB10 | 26.9 | 23.2 |
| Gemma 4 26B-A4B | B200 | 159.6 | 220.2 |
| Gemma 4 26B-A4B | H100 SXM | 138.7 | 148.0 |

</details>

Why mistral.rs? Copy Chapter

- Automatic model loading: Architecture, weight format, and chat template are detected for supported Hugging Face models and GGUF files, with flags available for explicit selection.
- True multimodality: Text, vision, video, and audio, speech generation, image generation, and embeddings in one engine.
- Quantization selection: --quant selects a matching artifact from GGUF repositories. For other Hugging Face repositories, it uses a prebuilt UQFF when available and otherwise applies ISQ. Docs
- OpenAI + Anthropic compatible serving: The same mistralrs serve process exposes OpenAI-compatible /v1 endpoints and Anthropic-compatible Messages endpoints.
- Prometheus metrics: mistralrs serve exposes a /metrics endpoint in Prometheus format, recording per-request counts and latency labeled by method, route, and status. Docs
- Built-in web UI: Served at /ui by default. Shows reasoning, code execution, plots, and files inline. Edit any message and the new branch runs with its own Python state. Pass --no-ui to disable.
- Hardware-aware: mistralrs tune recommends quantization and device mapping from the model config and your detected hardware.
- Flexible SDKs: Python package and Rust crate to build your projects.
- Native agentic support: built-in agentic loop with web search, local Python code execution, shell execution, OpenAI-compatible Skills, session management, and custom tool hooks.

Quick Start Copy Chapter

Install Copy Section

Linux/macOS:

curl --proto '=https' --tlsv1.2 -sSf https://raw.githubusercontent.com/EricLBuehler/mistral.rs/master/install.sh | sh

Windows (PowerShell):

irm https://raw.githubusercontent.com/EricLBuehler/mistral.rs/master/install.ps1 | iex

Downloads a self-contained prebuilt binary for your platform (Metal on Apple Silicon; per-GPU CUDA or CPU on Linux; CPU on Windows), falling back to a source build if none matches. Standard acceleration needs no Rust or CUDA toolkit. Optional cuTile acceleration requires NVIDIA's separately installed tileiras tool.

Manual installation, accelerator details & other platforms

Run Your First Model Copy Section

Interactive chat

mistralrs run -m Qwen/Qwen3-4B
One-shot prompt (no interactive session)

mistralrs run -m Qwen/Qwen3-4B -i "What is the capital of France?"
One-shot with an image

mistralrs run -m google/gemma-4-E4B-it --image photo.jpg -i "Describe this image"
Run a local GGUF or select a published 4-bit GGUF

mistralrs run -f /path/to/model.gguf
mistralrs run -m unsloth/Qwen3.5-4B-GGUF --quant 4
Agentic REPL: search + code execution + shell from the terminal

mistralrs run --agent -m Qwen/Qwen3-4B
Start an API server with the built-in web UI

mistralrs serve -m google/gemma-4-E4B-it

For the server command, visit http://localhost:1234/ui for the web chat interface. OpenAI-compatible clients use http://localhost:1234/v1; Anthropic-compatible clients use http://localhost:1234.

The mistralrs CLI mistralrs CLI')" title="Copy section prompt for LLMs"> Copy Section

The CLI uses the same run, serve, and bench commands for model repositories, local directories, and GGUF files.

- Auto-detection: Automatically detects model architecture, quantization format, and chat template
- All-in-one: Single binary for chat, server, benchmarks, and web UI (run, serve, bench)
- Hardware-aware tuning: mistralrs tune recommends quantization and device mapping for your model and hardware
- Model formats: Hugging Face checkpoints, GGUF files, and UQFF quantizations

Recommend settings for your hardware and emit a config file

mistralrs tune -m Qwen/Qwen3-4B --emit-config config.toml
Run using the generated config

mistralrs from-config -f config.toml
Diagnose system issues (CUDA, Metal, Hugging Face connectivity)

mistralrs doctor

Full CLI documentation

<details open>
<summary><b>UI Demo</b></summary>
<br>
<img src="https://raw.githubusercontent.com/EricLBuehler/mistral.rs/master/res/ui.gif" alt="UI Demo" />
</details>

What Makes It Fast Copy Chapter

Performance
- Continuous batching support by default on all devices.
- CUDA with FlashAttention V2/V3, Metal, and multi-GPU/distributed inference
- PagedAttention for high throughput continuous batching on CUDA or Apple Silicon, prefix caching (including multimodal)

Quantization (full docs)
- In-situ quantization (ISQ) for Hugging Face models
- GGUF (2-8 bit), GPTQ, AWQ, HQQ, FP8, BNB support
- ⭐ Per-layer topology: Fine-tune quantization per layer for optimal quality/speed
- ⭐ Auto-select fastest quant method for your hardware

Flexibility
- LoRA & X-LoRA with per-request LoRA selection and X-LoRA adapter mixing
- AnyMoE: Create mixture-of-experts on any base model
- Multiple models: Load/unload at runtime

Agentic Features
- Integrated tool calling with grammar enforcement and strict schema mode
- ⭐ Server-side agentic loop: auto-execute tools and feed results back
- ⭐ Python code execution: persistent Jupyter-like sessions with matplotlib capture and multimodal feedback
- ⭐ Shell execution: persistent command-line sessions with sandboxing and approval controls
- ⭐ OpenAI-compatible Skills: uploaded skill bundles for Responses API agents
- ⭐ OpenAI-compatible file inputs: /v1/files, Responses input_file, Chat file, and workdir mounts
- ⭐ Web search integration with embedding-based ranking
- ⭐ Tool dispatch URL: POST tool calls to your own endpoint
- ⭐ MCP client: Connect to external tools via Process, HTTP, or WebSocket
- Python/Rust tool callbacks for custom execution

Full feature documentation

Supported Models Copy Chapter

Text, multimodal, speech, image generation, and embedding models across 45+ architectures. The supported models reference is the single source of truth: it explains how to check whether your model's config.json is supported, lists every architecture with copy-paste run commands, and is generated directly from the engine's loader registry so it never drifts.

Supported models reference | Request a new model

Python SDK Copy Chapter

pip install mistralrs

In-process inference from Python: load a model with Runner and send OpenAI-shaped requests, no server required. Accelerator-specific wheels (CUDA, Metal, MKL, Accelerate) are listed in the getting-started guide.

Get started | API reference | Examples

Rust SDK Copy Chapter

cargo add mistralrs

Embed the engine in a Rust application with the high-level mistralrs crate.

Get started | docs.rs | Crate | Examples

Docker Copy Chapter

Prebuilt CPU and CUDA images are published to GHCR. Pull commands, tags, and Kubernetes notes are in the Docker guide.

Documentation Copy Chapter

For complete documentation, see the Documentation.

Quick Links:
- Quickstart - Install, first run, first serve
- CLI Reference - All commands and options
- Anthropic Messages API - Anthropic-compatible Messages, streaming, tool use, and token counting
- HTTP API - OpenAI-compatible and Anthropic-compatible endpoints
- Quantization - ISQ, GGUF, GPTQ, and more
- Multi-GPU and Distributed - NCCL TP, P2P layer mapping, multi-node, and ring
- MCP Integration - MCP integration documentation
- Troubleshooting - Common issues and solutions
- Environment variables - Environment variables for configuration

Contributing Copy Chapter

Contributions welcome! Please open an issue to discuss new features or report bugs. If you want to add a new model, please contact us via an issue and we can coordinate.

Credits Copy Chapter

This project would not be possible without the excellent work at Candle. Thank you to all contributors!

mistral.rs is not affiliated with Mistral AI.

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